Execution layer for
enterprise agents.

Give your agents the tools to get work done.
Set permissions and follow every run in one place.

Start Using Agents Build with Agent API
OpenAI Agents API compatible. Model agnostic.

Agents API showcases

See what you can build

Two open-source applications, from a shopping assistant to a personal agent. Explore the products and see how they use Rebyte.

Commerce

Commerce Agent

Turn a shopping conversation into a cart.

Find products, compare options, and update a cart inside the ACME storefront. Rebyte runs the Agent; the application handles catalog, cart, and presentation tools.

Sessions · Function tools · Skills · Tool search

How it works View source

Personal agent

Impo

An agent with context from your day.

Chat, delegate tasks, and get personal briefings. Echo captures spoken context for transcripts and memory; Rebyte powers the Agent behind conversations and tasks.

Sessions · Event streaming · Function tools

How it works Explore Impo
Read the story: two apps built with Rebyte

Run

Run Agents in the cloud

Give every Agent durable execution and the tools to finish real work, without building or operating the execution stack.

Durable execution
Keep context and continue work across Conversations.
Real tools
Give Agents browsers, files, compute, Skills, and apps.
Any surface
Run the same Agent through your API, product, CLI, or Slack.

Control

Control what it can do

Set capabilities and boundaries before work begins. Rebyte enforces the same policy across every run and every surface.

  • Slack
  • Gmail
  • Notion
  • Google Drive
  • Salesforce
  • Microsoft Teams

Skills, MCP, and hundreds of connected apps

Capabilities
Approve the models, Skills, MCP servers, tools, and apps it may use.
Boundaries
Restrict network access, credentials, execution steps, and spend.
Policy
Define the Agent once and apply the same controls to every run.

Observe & improve

See what happened.
Improve what happens next.

Turn production runs into a feedback loop. Observe the execution, annotate the outcome, and help the Agent improve over time.

Observe
Trace every model step, tool call, error, token, and cost.
Annotate
Mark the outcomes worth repeating and the ones that need correction.
Improve
Feed production evidence back into instructions, Skills, and evaluations.
Production Agent trace showing model steps and tool calls
A complete production run, from first model step to final response.

Run your first Agent

Get an organization API key, define an Agent, and start a Session. Add Skills, MCP servers, and Runtime controls when the job needs them.

Get an API key Read the quickstart